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Wanghui Qiu

Master's Student
East China Normal University
onehui@stu.ecnu.edu.cn


About Me

I am currently a Master’s student at the School of Data Science and Engineering of East China Normal University and a member of the Decision Intelligence Lab, advised by Prof. Bin Yang.

My research interests cover Time Series Analysis and Deep Learning. I am currently working on foundation time series models, large time series models, and multi-modal time series models. In addition to pure research, I also dedicate myself to promoting research on valuable real-world applications. My research aims to contribute to the advancement of intelligent systems capable of handling massive and complicated temporal data across domains, including finance, healthcare, industry, and environment.

🔍 Research Topics

My research interests cover Time Series Analysis and Deep Learning. I am currently working on foundation time series models, and time series benchmarking. In addition to pure research, I also dedicate myself to promoting research on valuable real-world applications. My research aims to contribute to the advancement of intelligent systems capable of handling massive and complicated temporal data across domains, including finance, industry, and environment.

📝 Publications & Preprints

  1. PVLDB
    TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
    Xiangfei Qiu, Zhe Li, Wanghui Qiu, Shiyan Hu, Lekui Zhou, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Aoying Zhou, Zhenli Sheng, Jilin Hu, Christian S. Jensen, Bin Yang
    International Conference on Very Large Databases, 2025.
    PDF Code PVLDB, CCF A, Accepted

  2. CIKM
    Land Deformation Prediction via Multi-modal Adaptive Association Learning
    Wanghui Qiu, Shiyan Hu, Chenjuan Guo, Wenbing Shi, Lina Yu, Ming Gao, Aoying Zhou, Bin Yang
    Conference on Information and Knowledge Management, 2026
    PDF CIKM, CCF B, Accepted

💻 System & Applications

  1. TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
    A comprehensive and fair benchmarking framework for time series forecasting methods, addressing key challenges in evaluation and comparison.
    PDF Code

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